Overview

Dataset statistics

Number of variables21
Number of observations23547
Missing cells66918
Missing cells (%)13.5%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory3.8 MiB
Average record size in memory168.0 B

Variable types

Categorical8
Numeric13

Warnings

Dataset has 1 (< 0.1%) duplicate rows Duplicates
Suburb has a high cardinality: 336 distinct values High cardinality
Address has a high cardinality: 23108 distinct values High cardinality
SellerG has a high cardinality: 330 distinct values High cardinality
Date has a high cardinality: 58 distinct values High cardinality
Rooms is highly correlated with Bedroom2High correlation
Bedroom2 is highly correlated with RoomsHigh correlation
Price has 5151 (21.9%) missing values Missing
Bedroom2 has 4481 (19.0%) missing values Missing
Bathroom has 4484 (19.0%) missing values Missing
Car has 4626 (19.6%) missing values Missing
Landsize has 6137 (26.1%) missing values Missing
BuildingArea has 13529 (57.5%) missing values Missing
YearBuilt has 12007 (51.0%) missing values Missing
CouncilArea has 7891 (33.5%) missing values Missing
Lattitude has 4304 (18.3%) missing values Missing
Longtitude has 4304 (18.3%) missing values Missing
Landsize is highly skewed (γ1 = 106.0673053) Skewed
BuildingArea is highly skewed (γ1 = 88.59840308) Skewed
Address is uniformly distributed Uniform
Car has 1385 (5.9%) zeros Zeros
Landsize has 2437 (10.3%) zeros Zeros

Reproduction

Analysis started2021-04-11 07:41:53.505870
Analysis finished2021-04-11 07:42:15.901414
Duration22.4 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

Suburb
Categorical

HIGH CARDINALITY

Distinct336
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size184.1 KiB
Reservoir
 
629
Bentleigh East
 
429
Richmond
 
416
Glen Iris
 
378
Preston
 
357
Other values (331)
21338 

Length

Max length18
Median length9
Mean length9.783029685
Min length3

Characters and Unicode

Total characters230361
Distinct characters49
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)0.1%

Sample

1st rowAbbotsford
2nd rowAbbotsford
3rd rowAbbotsford
4th rowAbbotsford
5th rowAbbotsford
ValueCountFrequency (%)
Reservoir629
 
2.7%
Bentleigh East429
 
1.8%
Richmond416
 
1.8%
Glen Iris378
 
1.6%
Preston357
 
1.5%
Kew357
 
1.5%
Brighton348
 
1.5%
South Yarra331
 
1.4%
Brunswick330
 
1.4%
Hawthorn318
 
1.4%
Other values (326)19654
83.5%
2021-04-11T13:12:16.121855image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
east1964
 
6.0%
north1277
 
3.9%
south913
 
2.8%
melbourne789
 
2.4%
west786
 
2.4%
bentleigh663
 
2.0%
brunswick656
 
2.0%
brighton641
 
2.0%
reservoir629
 
1.9%
balwyn548
 
1.7%
Other values (283)23600
72.7%

Most occurring characters

ValueCountFrequency (%)
e20686
 
9.0%
r19501
 
8.5%
o19438
 
8.4%
n16683
 
7.2%
a15593
 
6.8%
t14219
 
6.2%
l12814
 
5.6%
i10763
 
4.7%
s10712
 
4.7%
8919
 
3.9%
Other values (39)81033
35.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter188923
82.0%
Uppercase Letter32519
 
14.1%
Space Separator8919
 
3.9%

Most frequent character per category

ValueCountFrequency (%)
e20686
10.9%
r19501
10.3%
o19438
10.3%
n16683
8.8%
a15593
 
8.3%
t14219
 
7.5%
l12814
 
6.8%
i10763
 
5.7%
s10712
 
5.7%
h7734
 
4.1%
Other values (15)40780
21.6%
ValueCountFrequency (%)
B3506
 
10.8%
E2884
 
8.9%
M2778
 
8.5%
S2453
 
7.5%
H2408
 
7.4%
C2275
 
7.0%
P2055
 
6.3%
N1974
 
6.1%
W1661
 
5.1%
A1649
 
5.1%
Other values (13)8876
27.3%
ValueCountFrequency (%)
8919
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin221442
96.1%
Common8919
 
3.9%

Most frequent character per script

ValueCountFrequency (%)
e20686
 
9.3%
r19501
 
8.8%
o19438
 
8.8%
n16683
 
7.5%
a15593
 
7.0%
t14219
 
6.4%
l12814
 
5.8%
i10763
 
4.9%
s10712
 
4.8%
h7734
 
3.5%
Other values (38)73299
33.1%
ValueCountFrequency (%)
8919
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII230361
100.0%

Most frequent character per block

ValueCountFrequency (%)
e20686
 
9.0%
r19501
 
8.5%
o19438
 
8.4%
n16683
 
7.2%
a15593
 
6.8%
t14219
 
6.2%
l12814
 
5.6%
i10763
 
4.7%
s10712
 
4.7%
8919
 
3.9%
Other values (39)81033
35.2%

Address
Categorical

HIGH CARDINALITY
UNIFORM

Distinct23108
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Memory size184.1 KiB
5 Charles St
 
5
13 Robinson St
 
3
16 Smith St
 
3
36 Aberfeldie St
 
3
7 Churchill Av
 
3
Other values (23103)
23530 

Length

Max length27
Median length13
Mean length13.60394105
Min length8

Characters and Unicode

Total characters320332
Distinct characters64
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22692 ?
Unique (%)96.4%

Sample

1st row68 Studley St
2nd row85 Turner St
3rd row25 Bloomburg St
4th row18/659 Victoria St
5th row5 Charles St
ValueCountFrequency (%)
5 Charles St5
 
< 0.1%
13 Robinson St3
 
< 0.1%
16 Smith St3
 
< 0.1%
36 Aberfeldie St3
 
< 0.1%
7 Churchill Av3
 
< 0.1%
3 Charles St3
 
< 0.1%
7 Hope St3
 
< 0.1%
38 Stewart St3
 
< 0.1%
5 Margaret St3
 
< 0.1%
1/1 Clarendon St3
 
< 0.1%
Other values (23098)23515
99.9%
2021-04-11T13:12:16.416058image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
st12272
 
17.3%
rd4565
 
6.4%
av2260
 
3.2%
ct965
 
1.4%
cr719
 
1.0%
dr692
 
1.0%
gr481
 
0.7%
3445
 
0.6%
5428
 
0.6%
4422
 
0.6%
Other values (10156)47735
67.2%

Most occurring characters

ValueCountFrequency (%)
47437
 
14.8%
t20425
 
6.4%
e16701
 
5.2%
r15173
 
4.7%
a14749
 
4.6%
S13919
 
4.3%
n12834
 
4.0%
112474
 
3.9%
o11622
 
3.6%
l11030
 
3.4%
Other values (54)143968
44.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter161156
50.3%
Decimal Number56174
 
17.5%
Uppercase Letter48256
 
15.1%
Space Separator47437
 
14.8%
Other Punctuation7309
 
2.3%

Most frequent character per category

ValueCountFrequency (%)
S13919
28.8%
R5811
12.0%
C4025
 
8.3%
A4009
 
8.3%
B2450
 
5.1%
M2206
 
4.6%
P1847
 
3.8%
D1831
 
3.8%
G1759
 
3.6%
W1645
 
3.4%
Other values (16)8754
18.1%
ValueCountFrequency (%)
t20425
12.7%
e16701
10.4%
r15173
9.4%
a14749
9.2%
n12834
 
8.0%
o11622
 
7.2%
l11030
 
6.8%
d9990
 
6.2%
i8838
 
5.5%
s5941
 
3.7%
Other values (16)33853
21.0%
ValueCountFrequency (%)
112474
22.2%
28570
15.3%
36697
11.9%
45403
9.6%
54694
 
8.4%
64164
 
7.4%
73782
 
6.7%
03757
 
6.7%
83522
 
6.3%
93111
 
5.5%
ValueCountFrequency (%)
47437
100.0%
ValueCountFrequency (%)
/7309
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin209412
65.4%
Common110920
34.6%

Most frequent character per script

ValueCountFrequency (%)
t20425
 
9.8%
e16701
 
8.0%
r15173
 
7.2%
a14749
 
7.0%
S13919
 
6.6%
n12834
 
6.1%
o11622
 
5.5%
l11030
 
5.3%
d9990
 
4.8%
i8838
 
4.2%
Other values (42)74131
35.4%
ValueCountFrequency (%)
47437
42.8%
112474
 
11.2%
28570
 
7.7%
/7309
 
6.6%
36697
 
6.0%
45403
 
4.9%
54694
 
4.2%
64164
 
3.8%
73782
 
3.4%
03757
 
3.4%
Other values (2)6633
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII320332
100.0%

Most frequent character per block

ValueCountFrequency (%)
47437
 
14.8%
t20425
 
6.4%
e16701
 
5.2%
r15173
 
4.7%
a14749
 
4.6%
S13919
 
4.3%
n12834
 
4.0%
112474
 
3.9%
o11622
 
3.6%
l11030
 
3.4%
Other values (54)143968
44.9%

Rooms
Real number (ℝ≥0)

HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.976047904
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Memory size184.1 KiB
2021-04-11T13:12:16.522272image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile5
Maximum12
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.9745005716
Coefficient of variation (CV)0.3274478782
Kurtosis1.820956665
Mean2.976047904
Median Absolute Deviation (MAD)1
Skewness0.502241333
Sum70077
Variance0.9496513641
MonotocityNot monotonic
2021-04-11T13:12:16.601752image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
310076
42.8%
26115
26.0%
44953
21.0%
11119
 
4.8%
51107
 
4.7%
6133
 
0.6%
719
 
0.1%
814
 
0.1%
105
 
< 0.1%
94
 
< 0.1%
ValueCountFrequency (%)
11119
 
4.8%
26115
26.0%
310076
42.8%
44953
21.0%
51107
 
4.7%
ValueCountFrequency (%)
122
 
< 0.1%
105
 
< 0.1%
94
 
< 0.1%
814
0.1%
719
0.1%

Type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size184.1 KiB
h
15760 
u
5280 
t
2507 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23547
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowh
2nd rowh
3rd rowh
4th rowu
5th rowh
ValueCountFrequency (%)
h15760
66.9%
u5280
 
22.4%
t2507
 
10.6%
2021-04-11T13:12:16.801130image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-11T13:12:16.870971image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
h15760
66.9%
u5280
 
22.4%
t2507
 
10.6%

Most occurring characters

ValueCountFrequency (%)
h15760
66.9%
u5280
 
22.4%
t2507
 
10.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter23547
100.0%

Most frequent character per category

ValueCountFrequency (%)
h15760
66.9%
u5280
 
22.4%
t2507
 
10.6%

Most occurring scripts

ValueCountFrequency (%)
Latin23547
100.0%

Most frequent character per script

ValueCountFrequency (%)
h15760
66.9%
u5280
 
22.4%
t2507
 
10.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII23547
100.0%

Most frequent character per block

ValueCountFrequency (%)
h15760
66.9%
u5280
 
22.4%
t2507
 
10.6%

Price
Real number (ℝ≥0)

MISSING

Distinct2470
Distinct (%)13.4%
Missing5151
Missing (%)21.9%
Infinite0
Infinite (%)0.0%
Mean1056697.461
Minimum85000
Maximum9000000
Zeros0
Zeros (%)0.0%
Memory size184.1 KiB
2021-04-11T13:12:16.969086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum85000
5-th percentile405000
Q1633000
median880000
Q31302000
95-th percentile2255000
Maximum9000000
Range8915000
Interquartile range (IQR)669000

Descriptive statistics

Standard deviation641921.6667
Coefficient of variation (CV)0.6074791418
Kurtosis10.37309885
Mean1056697.461
Median Absolute Deviation (MAD)306500
Skewness2.366672689
Sum1.943900649 × 1010
Variance4.120634262 × 1011
MonotocityNot monotonic
2021-04-11T13:12:17.094449image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600000156
 
0.7%
1100000151
 
0.6%
650000149
 
0.6%
800000144
 
0.6%
1200000144
 
0.6%
1300000140
 
0.6%
1000000134
 
0.6%
900000123
 
0.5%
500000116
 
0.5%
750000116
 
0.5%
Other values (2460)17023
72.3%
(Missing)5151
 
21.9%
ValueCountFrequency (%)
850001
< 0.1%
1210001
< 0.1%
1310001
< 0.1%
1450002
< 0.1%
1600001
< 0.1%
ValueCountFrequency (%)
90000001
< 0.1%
80000001
< 0.1%
76500001
< 0.1%
68000001
< 0.1%
65000001
< 0.1%

Method
Categorical

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size184.1 KiB
S
13660 
SP
3366 
PI
3140 
VB
1861 
SN
 
1041
Other values (4)
 
479

Length

Max length2
Median length1
Mean length1.415891621
Min length1

Characters and Unicode

Total characters33340
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSS
2nd rowS
3rd rowS
4th rowVB
5th rowSP
ValueCountFrequency (%)
S13660
58.0%
SP3366
 
14.3%
PI3140
 
13.3%
VB1861
 
7.9%
SN1041
 
4.4%
PN209
 
0.9%
SA154
 
0.7%
W94
 
0.4%
SS22
 
0.1%
2021-04-11T13:12:17.315289image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-11T13:12:17.386624image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
s13660
58.0%
sp3366
 
14.3%
pi3140
 
13.3%
vb1861
 
7.9%
sn1041
 
4.4%
pn209
 
0.9%
sa154
 
0.7%
w94
 
0.4%
ss22
 
0.1%

Most occurring characters

ValueCountFrequency (%)
S18265
54.8%
P6715
 
20.1%
I3140
 
9.4%
V1861
 
5.6%
B1861
 
5.6%
N1250
 
3.7%
A154
 
0.5%
W94
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter33340
100.0%

Most frequent character per category

ValueCountFrequency (%)
S18265
54.8%
P6715
 
20.1%
I3140
 
9.4%
V1861
 
5.6%
B1861
 
5.6%
N1250
 
3.7%
A154
 
0.5%
W94
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin33340
100.0%

Most frequent character per script

ValueCountFrequency (%)
S18265
54.8%
P6715
 
20.1%
I3140
 
9.4%
V1861
 
5.6%
B1861
 
5.6%
N1250
 
3.7%
A154
 
0.5%
W94
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII33340
100.0%

Most frequent character per block

ValueCountFrequency (%)
S18265
54.8%
P6715
 
20.1%
I3140
 
9.4%
V1861
 
5.6%
B1861
 
5.6%
N1250
 
3.7%
A154
 
0.5%
W94
 
0.3%

SellerG
Categorical

HIGH CARDINALITY

Distinct330
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size184.1 KiB
Nelson
2374 
Jellis
2320 
Barry
1998 
hockingstuart
1943 
Marshall
1474 
Other values (325)
13438 

Length

Max length27
Median length6
Mean length6.384380176
Min length1

Characters and Unicode

Total characters150333
Distinct characters58
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique86 ?
Unique (%)0.4%

Sample

1st rowJellis
2nd rowBiggin
3rd rowBiggin
4th rowRounds
5th rowBiggin
ValueCountFrequency (%)
Nelson2374
 
10.1%
Jellis2320
 
9.9%
Barry1998
 
8.5%
hockingstuart1943
 
8.3%
Marshall1474
 
6.3%
Ray1251
 
5.3%
Buxton1242
 
5.3%
Biggin665
 
2.8%
Fletchers571
 
2.4%
Woodards510
 
2.2%
Other values (320)9199
39.1%
2021-04-11T13:12:17.640552image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nelson2374
 
10.1%
jellis2320
 
9.9%
barry1998
 
8.5%
hockingstuart1943
 
8.3%
marshall1474
 
6.3%
ray1251
 
5.3%
buxton1242
 
5.3%
biggin665
 
2.8%
fletchers571
 
2.4%
woodards510
 
2.2%
Other values (317)9199
39.1%

Most occurring characters

ValueCountFrequency (%)
l13679
 
9.1%
a13061
 
8.7%
r12158
 
8.1%
s11803
 
7.9%
e10765
 
7.2%
o9423
 
6.3%
n8665
 
5.8%
i8360
 
5.6%
t7064
 
4.7%
h5197
 
3.5%
Other values (48)50158
33.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter125664
83.6%
Uppercase Letter24175
 
16.1%
Other Punctuation304
 
0.2%
Decimal Number190
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
B4841
20.0%
N2857
11.8%
J2764
11.4%
M2564
10.6%
R2362
9.8%
G1111
 
4.6%
W985
 
4.1%
H800
 
3.3%
F689
 
2.9%
T666
 
2.8%
Other values (16)4536
18.8%
ValueCountFrequency (%)
l13679
10.9%
a13061
10.4%
r12158
9.7%
s11803
9.4%
e10765
8.6%
o9423
 
7.5%
n8665
 
6.9%
i8360
 
6.7%
t7064
 
5.6%
h5197
 
4.1%
Other values (15)25489
20.3%
ValueCountFrequency (%)
'177
58.2%
.53
 
17.4%
&48
 
15.8%
/20
 
6.6%
@6
 
2.0%
ValueCountFrequency (%)
295
50.0%
195
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin149839
99.7%
Common494
 
0.3%

Most frequent character per script

ValueCountFrequency (%)
l13679
 
9.1%
a13061
 
8.7%
r12158
 
8.1%
s11803
 
7.9%
e10765
 
7.2%
o9423
 
6.3%
n8665
 
5.8%
i8360
 
5.6%
t7064
 
4.7%
h5197
 
3.5%
Other values (41)49664
33.1%
ValueCountFrequency (%)
'177
35.8%
295
19.2%
195
19.2%
.53
 
10.7%
&48
 
9.7%
/20
 
4.0%
@6
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII150333
100.0%

Most frequent character per block

ValueCountFrequency (%)
l13679
 
9.1%
a13061
 
8.7%
r12158
 
8.1%
s11803
 
7.9%
e10765
 
7.2%
o9423
 
6.3%
n8665
 
5.8%
i8360
 
5.6%
t7064
 
4.7%
h5197
 
3.5%
Other values (48)50158
33.4%

Date
Categorical

HIGH CARDINALITY

Distinct58
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size184.1 KiB
27-05-2017
 
770
23-09-2017
 
742
16-09-2017
 
730
03-06-2017
 
689
26-08-2017
 
647
Other values (53)
19969 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters235470
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row03-09-2016
2nd row03-12-2016
3rd row04-02-2016
4th row04-02-2016
5th row04-03-2017
ValueCountFrequency (%)
27-05-2017770
 
3.3%
23-09-2017742
 
3.2%
16-09-2017730
 
3.1%
03-06-2017689
 
2.9%
26-08-2017647
 
2.7%
17-06-2017637
 
2.7%
24-06-2017607
 
2.6%
09-09-2017598
 
2.5%
27-11-2016575
 
2.4%
03-09-2017567
 
2.4%
Other values (48)16985
72.1%
2021-04-11T13:12:17.868056image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
27-05-2017770
 
3.3%
23-09-2017742
 
3.2%
16-09-2017730
 
3.1%
03-06-2017689
 
2.9%
26-08-2017647
 
2.7%
17-06-2017637
 
2.7%
24-06-2017607
 
2.6%
09-09-2017598
 
2.5%
27-11-2016575
 
2.4%
03-09-2017567
 
2.4%
Other values (48)16985
72.1%

Most occurring characters

ValueCountFrequency (%)
052515
22.3%
-47094
20.0%
137925
16.1%
236115
15.3%
719655
 
8.3%
616089
 
6.8%
96487
 
2.8%
85955
 
2.5%
35007
 
2.1%
54858
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number188376
80.0%
Dash Punctuation47094
 
20.0%

Most frequent character per category

ValueCountFrequency (%)
052515
27.9%
137925
20.1%
236115
19.2%
719655
 
10.4%
616089
 
8.5%
96487
 
3.4%
85955
 
3.2%
35007
 
2.7%
54858
 
2.6%
43770
 
2.0%
ValueCountFrequency (%)
-47094
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common235470
100.0%

Most frequent character per script

ValueCountFrequency (%)
052515
22.3%
-47094
20.0%
137925
16.1%
236115
15.3%
719655
 
8.3%
616089
 
6.8%
96487
 
2.8%
85955
 
2.5%
35007
 
2.1%
54858
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII235470
100.0%

Most frequent character per block

ValueCountFrequency (%)
052515
22.3%
-47094
20.0%
137925
16.1%
236115
15.3%
719655
 
8.3%
616089
 
6.8%
96487
 
2.8%
85955
 
2.5%
35007
 
2.1%
54858
 
2.1%

Distance
Real number (ℝ≥0)

Distinct211
Distinct (%)0.9%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean10.30651491
Minimum0
Maximum48.1
Zeros32
Zeros (%)0.1%
Memory size184.1 KiB
2021-04-11T13:12:17.964390image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.6
Q16.2
median9.5
Q313
95-th percentile21.1
Maximum48.1
Range48.1
Interquartile range (IQR)6.8

Descriptive statistics

Standard deviation6.016318012
Coefficient of variation (CV)0.5837393208
Kurtosis5.16715539
Mean10.30651491
Median Absolute Deviation (MAD)3.5
Skewness1.674115852
Sum242677.2
Variance36.19608242
MonotocityNot monotonic
2021-04-11T13:12:18.079024image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.21272
 
5.4%
9.2665
 
2.8%
7.8570
 
2.4%
13.9499
 
2.1%
4.6473
 
2.0%
13427
 
1.8%
13.8425
 
1.8%
10.5395
 
1.7%
5.2383
 
1.6%
11.4382
 
1.6%
Other values (201)18055
76.7%
ValueCountFrequency (%)
032
0.1%
0.716
 
0.1%
1.247
0.2%
1.315
 
0.1%
1.44
 
< 0.1%
ValueCountFrequency (%)
48.14
 
< 0.1%
47.43
 
< 0.1%
47.39
< 0.1%
45.914
0.1%
45.21
 
< 0.1%

Postcode
Real number (ℝ≥0)

Distinct206
Distinct (%)0.9%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3109.782893
Minimum3000
Maximum3978
Zeros0
Zeros (%)0.0%
Memory size184.1 KiB
2021-04-11T13:12:18.192494image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum3000
5-th percentile3013
Q13047
median3101
Q33150
95-th percentile3204
Maximum3978
Range978
Interquartile range (IQR)103

Descriptive statistics

Standard deviation94.52218971
Coefficient of variation (CV)0.03039510891
Kurtosis28.59355041
Mean3109.782893
Median Absolute Deviation (MAD)50
Skewness4.163565827
Sum73222948
Variance8934.444347
MonotocityNot monotonic
2021-04-11T13:12:18.731442image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3073629
 
2.7%
3046490
 
2.1%
3020460
 
2.0%
3121457
 
1.9%
3165429
 
1.8%
3058414
 
1.8%
3163411
 
1.7%
3040404
 
1.7%
3032380
 
1.6%
3204379
 
1.6%
Other values (196)19093
81.1%
ValueCountFrequency (%)
3000159
0.7%
300244
 
0.2%
300353
 
0.2%
300663
 
0.3%
300814
 
0.1%
ValueCountFrequency (%)
39783
 
< 0.1%
397714
0.1%
39767
< 0.1%
39751
 
< 0.1%
391011
< 0.1%

Bedroom2
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct13
Distinct (%)0.1%
Missing4481
Missing (%)19.0%
Infinite0
Infinite (%)0.0%
Mean2.951956362
Minimum0
Maximum30
Zeros17
Zeros (%)0.1%
Memory size184.1 KiB
2021-04-11T13:12:18.838824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum30
Range30
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.9960317812
Coefficient of variation (CV)0.3374141278
Kurtosis33.60731924
Mean2.951956362
Median Absolute Deviation (MAD)1
Skewness1.667158799
Sum56282
Variance0.9920793091
MonotocityNot monotonic
2021-04-11T13:12:18.919808image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
38207
34.9%
25059
21.5%
43831
16.3%
1942
 
4.0%
5873
 
3.7%
6104
 
0.4%
717
 
0.1%
017
 
0.1%
88
 
< 0.1%
95
 
< 0.1%
Other values (3)3
 
< 0.1%
(Missing)4481
19.0%
ValueCountFrequency (%)
017
 
0.1%
1942
 
4.0%
25059
21.5%
38207
34.9%
43831
16.3%
ValueCountFrequency (%)
301
 
< 0.1%
201
 
< 0.1%
101
 
< 0.1%
95
< 0.1%
88
< 0.1%

Bathroom
Real number (ℝ≥0)

MISSING

Distinct10
Distinct (%)0.1%
Missing4484
Missing (%)19.0%
Infinite0
Infinite (%)0.0%
Mean1.570896501
Minimum0
Maximum12
Zeros46
Zeros (%)0.2%
Memory size184.1 KiB
2021-04-11T13:12:19.003451image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum12
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7126836776
Coefficient of variation (CV)0.4536795881
Kurtosis5.263207479
Mean1.570896501
Median Absolute Deviation (MAD)0
Skewness1.433137743
Sum29946
Variance0.5079180243
MonotocityNot monotonic
2021-04-11T13:12:19.090247image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
110080
42.8%
27273
30.9%
31422
 
6.0%
4180
 
0.8%
551
 
0.2%
046
 
0.2%
65
 
< 0.1%
73
 
< 0.1%
82
 
< 0.1%
121
 
< 0.1%
(Missing)4484
19.0%
ValueCountFrequency (%)
046
 
0.2%
110080
42.8%
27273
30.9%
31422
 
6.0%
4180
 
0.8%
ValueCountFrequency (%)
121
 
< 0.1%
82
 
< 0.1%
73
 
< 0.1%
65
 
< 0.1%
551
0.2%

Car
Real number (ℝ≥0)

MISSING
ZEROS

Distinct13
Distinct (%)0.1%
Missing4626
Missing (%)19.6%
Infinite0
Infinite (%)0.0%
Mean1.6262354
Minimum0
Maximum26
Zeros1385
Zeros (%)5.9%
Memory size184.1 KiB
2021-04-11T13:12:19.172105image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q32
95-th percentile3
Maximum26
Range26
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9740484799
Coefficient of variation (CV)0.5989590929
Kurtosis25.63523817
Mean1.6262354
Median Absolute Deviation (MAD)1
Skewness2.136242257
Sum30770
Variance0.9487704412
MonotocityNot monotonic
2021-04-11T13:12:19.255504image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
28088
34.3%
17513
31.9%
01385
 
5.9%
31045
 
4.4%
4693
 
2.9%
587
 
0.4%
679
 
0.3%
814
 
0.1%
711
 
< 0.1%
103
 
< 0.1%
Other values (3)3
 
< 0.1%
(Missing)4626
19.6%
ValueCountFrequency (%)
01385
 
5.9%
17513
31.9%
28088
34.3%
31045
 
4.4%
4693
 
2.9%
ValueCountFrequency (%)
261
 
< 0.1%
111
 
< 0.1%
103
 
< 0.1%
91
 
< 0.1%
814
0.1%

Landsize
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct1567
Distinct (%)9.0%
Missing6137
Missing (%)26.1%
Infinite0
Infinite (%)0.0%
Mean551.7834578
Minimum0
Maximum433014
Zeros2437
Zeros (%)10.3%
Memory size184.1 KiB
2021-04-11T13:12:19.357837image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1181
median448
Q3656
95-th percentile1005
Maximum433014
Range433014
Interquartile range (IQR)475

Descriptive statistics

Standard deviation3544.288014
Coefficient of variation (CV)6.423331406
Kurtosis12762.56697
Mean551.7834578
Median Absolute Deviation (MAD)238
Skewness106.0673053
Sum9606550
Variance12561977.52
MonotocityNot monotonic
2021-04-11T13:12:19.471449image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02437
 
10.3%
650125
 
0.5%
69789
 
0.4%
58567
 
0.3%
70058
 
0.2%
69654
 
0.2%
59050
 
0.2%
53449
 
0.2%
60046
 
0.2%
60446
 
0.2%
Other values (1557)14389
61.1%
(Missing)6137
26.1%
ValueCountFrequency (%)
02437
10.3%
13
 
< 0.1%
21
 
< 0.1%
32
 
< 0.1%
51
 
< 0.1%
ValueCountFrequency (%)
4330141
< 0.1%
760001
< 0.1%
751001
< 0.1%
445001
< 0.1%
414001
< 0.1%

BuildingArea
Real number (ℝ≥0)

MISSING
SKEWED

Distinct688
Distinct (%)6.9%
Missing13529
Missing (%)57.5%
Infinite0
Infinite (%)0.0%
Mean154.5278952
Minimum0
Maximum44515
Zeros30
Zeros (%)0.1%
Memory size184.1 KiB
2021-04-11T13:12:19.594855image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile52
Q195
median129
Q3180
95-th percentile305
Maximum44515
Range44515
Interquartile range (IQR)85

Descriptive statistics

Standard deviation462.5357653
Coefficient of variation (CV)2.99321857
Kurtosis8454.277997
Mean154.5278952
Median Absolute Deviation (MAD)40
Skewness88.59840308
Sum1548060.454
Variance213939.3342
MonotocityNot monotonic
2021-04-11T13:12:19.709137image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120144
 
0.6%
100129
 
0.5%
110123
 
0.5%
130117
 
0.5%
115102
 
0.4%
15096
 
0.4%
12592
 
0.4%
11289
 
0.4%
8089
 
0.4%
14085
 
0.4%
Other values (678)8952
38.0%
(Missing)13529
57.5%
ValueCountFrequency (%)
030
0.1%
0.011
 
< 0.1%
113
0.1%
218
0.1%
323
0.1%
ValueCountFrequency (%)
445151
< 0.1%
67911
< 0.1%
46451
< 0.1%
36471
< 0.1%
35581
< 0.1%

YearBuilt
Real number (ℝ≥0)

MISSING

Distinct155
Distinct (%)1.3%
Missing12007
Missing (%)51.0%
Infinite0
Infinite (%)0.0%
Mean1964.636742
Minimum1196
Maximum2106
Zeros0
Zeros (%)0.0%
Memory size184.1 KiB
2021-04-11T13:12:19.821345image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1196
5-th percentile1900
Q11940
median1970
Q32000
95-th percentile2012
Maximum2106
Range910
Interquartile range (IQR)60

Descriptive statistics

Standard deviation37.59550363
Coefficient of variation (CV)0.0191361094
Kurtosis14.37586387
Mean1964.636742
Median Absolute Deviation (MAD)30
Skewness-1.228668418
Sum22671908
Variance1413.421893
MonotocityNot monotonic
2021-04-11T13:12:19.935455image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19701178
 
5.0%
1960971
 
4.1%
1950787
 
3.3%
1900480
 
2.0%
1980470
 
2.0%
2000449
 
1.9%
1930406
 
1.7%
1920405
 
1.7%
1890345
 
1.5%
1910330
 
1.4%
Other values (145)5719
24.3%
(Missing)12007
51.0%
ValueCountFrequency (%)
11961
 
< 0.1%
18001
 
< 0.1%
18301
 
< 0.1%
18504
< 0.1%
18542
< 0.1%
ValueCountFrequency (%)
21061
 
< 0.1%
20181
 
< 0.1%
201724
 
0.1%
201677
0.3%
201598
0.4%

CouncilArea
Categorical

MISSING

Distinct34
Distinct (%)0.2%
Missing7891
Missing (%)33.5%
Memory size184.1 KiB
Boroondara
1677 
Moreland
1421 
Stonnington
1141 
Moonee Valley
1141 
Darebin
1113 
Other values (29)
9163 

Length

Max length17
Median length9
Mean length9.085781809
Min length4

Characters and Unicode

Total characters142247
Distinct characters39
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowYarra
2nd rowYarra
3rd rowYarra
4th rowYarra
5th rowYarra
ValueCountFrequency (%)
Boroondara1677
 
7.1%
Moreland1421
 
6.0%
Stonnington1141
 
4.8%
Moonee Valley1141
 
4.8%
Darebin1113
 
4.7%
Glen Eira1019
 
4.3%
Port Phillip849
 
3.6%
Maribyrnong836
 
3.6%
Yarra836
 
3.6%
Banyule763
 
3.2%
Other values (24)4860
20.6%
(Missing)7891
33.5%
2021-04-11T13:12:20.174663image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
boroondara1677
 
8.7%
moreland1421
 
7.4%
valley1141
 
5.9%
moonee1141
 
5.9%
stonnington1141
 
5.9%
darebin1113
 
5.8%
glen1019
 
5.3%
eira1019
 
5.3%
yarra863
 
4.5%
phillip849
 
4.4%
Other values (29)7900
41.0%

Most occurring characters

ValueCountFrequency (%)
n17856
12.6%
o15986
11.2%
a15155
 
10.7%
r13007
 
9.1%
e11445
 
8.0%
i8321
 
5.8%
l8129
 
5.7%
M5022
 
3.5%
t4415
 
3.1%
d4114
 
2.9%
Other values (29)38797
27.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter119335
83.9%
Uppercase Letter19284
 
13.6%
Space Separator3628
 
2.6%

Most frequent character per category

ValueCountFrequency (%)
n17856
15.0%
o15986
13.4%
a15155
12.7%
r13007
10.9%
e11445
9.6%
i8321
7.0%
l8129
6.8%
t4415
 
3.7%
d4114
 
3.4%
y4099
 
3.4%
Other values (11)16808
14.1%
ValueCountFrequency (%)
M5022
26.0%
B4112
21.3%
P1698
 
8.8%
D1191
 
6.2%
V1141
 
5.9%
S1141
 
5.9%
G1097
 
5.7%
E1019
 
5.3%
Y863
 
4.5%
W756
 
3.9%
Other values (7)1244
 
6.5%
ValueCountFrequency (%)
3628
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin138619
97.4%
Common3628
 
2.6%

Most frequent character per script

ValueCountFrequency (%)
n17856
12.9%
o15986
11.5%
a15155
10.9%
r13007
 
9.4%
e11445
 
8.3%
i8321
 
6.0%
l8129
 
5.9%
M5022
 
3.6%
t4415
 
3.2%
d4114
 
3.0%
Other values (28)35169
25.4%
ValueCountFrequency (%)
3628
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII142247
100.0%

Most frequent character per block

ValueCountFrequency (%)
n17856
12.6%
o15986
11.2%
a15155
 
10.7%
r13007
 
9.1%
e11445
 
8.0%
i8321
 
5.8%
l8129
 
5.7%
M5022
 
3.5%
t4415
 
3.1%
d4114
 
2.9%
Other values (29)38797
27.3%

Lattitude
Real number (ℝ)

MISSING

Distinct8837
Distinct (%)45.9%
Missing4304
Missing (%)18.3%
Infinite0
Infinite (%)0.0%
Mean-37.81243432
Minimum-38.18418
Maximum-37.40758
Zeros0
Zeros (%)0.0%
Memory size184.1 KiB
2021-04-11T13:12:20.284691image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-38.18418
5-th percentile-37.935737
Q1-37.8593
median-37.8097
Q3-37.7598
95-th percentile-37.6987
Maximum-37.40758
Range0.7766
Interquartile range (IQR)0.0995

Descriptive statistics

Standard deviation0.07992583541
Coefficient of variation (CV)-0.002113744773
Kurtosis1.705319878
Mean-37.81243432
Median Absolute Deviation (MAD)0.0497
Skewness-0.3165347554
Sum-727624.6735
Variance0.006388139166
MonotocityNot monotonic
2021-04-11T13:12:20.408569image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-37.836125
 
0.1%
-37.842422
 
0.1%
-37.819820
 
0.1%
-37.795617
 
0.1%
-37.841417
 
0.1%
-37.796917
 
0.1%
-37.816116
 
0.1%
-37.84716
 
0.1%
-37.85116
 
0.1%
-37.763416
 
0.1%
Other values (8827)19061
80.9%
(Missing)4304
 
18.3%
ValueCountFrequency (%)
-38.184181
< 0.1%
-38.182551
< 0.1%
-38.181631
< 0.1%
-38.178291
< 0.1%
-38.177451
< 0.1%
ValueCountFrequency (%)
-37.407581
< 0.1%
-37.408531
< 0.1%
-37.413181
< 0.1%
-37.413811
< 0.1%
-37.414951
< 0.1%

Longtitude
Real number (ℝ≥0)

MISSING

Distinct9584
Distinct (%)49.8%
Missing4304
Missing (%)18.3%
Infinite0
Infinite (%)0.0%
Mean145.0002867
Minimum144.43162
Maximum145.52635
Zeros0
Zeros (%)0.0%
Memory size184.1 KiB
2021-04-11T13:12:20.543237image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum144.43162
5-th percentile144.833765
Q1144.9393
median145.0043
Q3145.0631
95-th percentile145.164607
Maximum145.52635
Range1.09473
Interquartile range (IQR)0.1238

Descriptive statistics

Standard deviation0.106070626
Coefficient of variation (CV)0.0007315201127
Kurtosis1.904059951
Mean145.0002867
Median Absolute Deviation (MAD)0.06159
Skewness-0.3123235122
Sum2790240.516
Variance0.01125097771
MonotocityNot monotonic
2021-04-11T13:12:20.666882image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
144.996620
 
0.1%
144.98516
 
0.1%
145.010416
 
0.1%
145.000116
 
0.1%
144.99116
 
0.1%
145.024316
 
0.1%
144.991115
 
0.1%
144.967915
 
0.1%
144.99715
 
0.1%
145.011614
 
0.1%
Other values (9574)19084
81.0%
(Missing)4304
 
18.3%
ValueCountFrequency (%)
144.431621
< 0.1%
144.431811
< 0.1%
144.485711
< 0.1%
144.540221
< 0.1%
144.542371
< 0.1%
ValueCountFrequency (%)
145.526351
< 0.1%
145.511371
< 0.1%
145.482731
< 0.1%
145.470521
< 0.1%
145.462711
< 0.1%

Regionname
Categorical

Distinct8
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size184.1 KiB
Southern Metropolitan
8772 
Northern Metropolitan
6480 
Western Metropolitan
4561 
Eastern Metropolitan
2640 
South-Eastern Metropolitan
 
857
Other values (3)
 
236

Length

Max length26
Median length21
Mean length20.8293553
Min length16

Characters and Unicode

Total characters490448
Distinct characters21
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorthern Metropolitan
2nd rowNorthern Metropolitan
3rd rowNorthern Metropolitan
4th rowNorthern Metropolitan
5th rowNorthern Metropolitan
ValueCountFrequency (%)
Southern Metropolitan8772
37.3%
Northern Metropolitan6480
27.5%
Western Metropolitan4561
19.4%
Eastern Metropolitan2640
 
11.2%
South-Eastern Metropolitan857
 
3.6%
Eastern Victoria107
 
0.5%
Northern Victoria78
 
0.3%
Western Victoria51
 
0.2%
(Missing)1
 
< 0.1%
2021-04-11T13:12:20.867936image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-11T13:12:20.930793image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
metropolitan23310
49.5%
southern8772
 
18.6%
northern6558
 
13.9%
western4612
 
9.8%
eastern2747
 
5.8%
south-eastern857
 
1.8%
victoria236
 
0.5%

Most occurring characters

ValueCountFrequency (%)
t71259
14.5%
o63043
12.9%
r53650
10.9%
e51468
10.5%
n46856
9.6%
a27150
 
5.5%
i23782
 
4.8%
23546
 
4.8%
M23310
 
4.8%
p23310
 
4.8%
Other values (11)83074
16.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter418096
85.2%
Uppercase Letter47949
 
9.8%
Space Separator23546
 
4.8%
Dash Punctuation857
 
0.2%

Most frequent character per category

ValueCountFrequency (%)
t71259
17.0%
o63043
15.1%
r53650
12.8%
e51468
12.3%
n46856
11.2%
a27150
 
6.5%
i23782
 
5.7%
p23310
 
5.6%
l23310
 
5.6%
h16187
 
3.9%
Other values (3)18081
 
4.3%
ValueCountFrequency (%)
M23310
48.6%
S9629
20.1%
N6558
 
13.7%
W4612
 
9.6%
E3604
 
7.5%
V236
 
0.5%
ValueCountFrequency (%)
23546
100.0%
ValueCountFrequency (%)
-857
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin466045
95.0%
Common24403
 
5.0%

Most frequent character per script

ValueCountFrequency (%)
t71259
15.3%
o63043
13.5%
r53650
11.5%
e51468
11.0%
n46856
10.1%
a27150
 
5.8%
i23782
 
5.1%
M23310
 
5.0%
p23310
 
5.0%
l23310
 
5.0%
Other values (9)58907
12.6%
ValueCountFrequency (%)
23546
96.5%
-857
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII490448
100.0%

Most frequent character per block

ValueCountFrequency (%)
t71259
14.5%
o63043
12.9%
r53650
10.9%
e51468
10.5%
n46856
9.6%
a27150
 
5.5%
i23782
 
4.8%
23546
 
4.8%
M23310
 
4.8%
p23310
 
4.8%
Other values (11)83074
16.9%

Propertycount
Real number (ℝ≥0)

Distinct330
Distinct (%)1.4%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean7517.480591
Minimum129
Maximum21650
Zeros0
Zeros (%)0.0%
Memory size184.1 KiB
2021-04-11T13:12:21.069600image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum129
5-th percentile2185
Q14385
median6567
Q310331
95-th percentile15321
Maximum21650
Range21521
Interquartile range (IQR)5946

Descriptive statistics

Standard deviation4414.995634
Coefficient of variation (CV)0.5872972441
Kurtosis1.138227076
Mean7517.480591
Median Absolute Deviation (MAD)2694
Skewness1.056708738
Sum177006598
Variance19492186.45
MonotocityNot monotonic
2021-04-11T13:12:21.187493image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21650629
 
2.7%
8870524
 
2.2%
10969429
 
1.8%
14949416
 
1.8%
10412378
 
1.6%
10331357
 
1.5%
14577357
 
1.5%
10579348
 
1.5%
14887331
 
1.4%
11918330
 
1.4%
Other values (320)19447
82.6%
ValueCountFrequency (%)
1291
 
< 0.1%
2491
 
< 0.1%
38911
< 0.1%
39416
0.1%
4389
< 0.1%
ValueCountFrequency (%)
21650629
2.7%
17496159
 
0.7%
173849
 
< 0.1%
1709321
 
0.1%
1705547
 
0.2%

Interactions

2021-04-11T13:11:56.513870image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:11:56.626421image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:11:56.731815image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:11:56.829255image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:11:57.111044image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:11:57.219812image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:11:57.312307image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:11:57.409985image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:11:57.528056image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:11:57.632926image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:11:57.761005image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:11:57.877387image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:11:57.980647image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:11:58.075033image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:11:58.175443image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:11:58.278251image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:11:58.390140image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:11:58.494098image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:11:58.584912image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:11:58.674867image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:11:58.765705image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:11:58.870945image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:11:58.983335image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:11:59.078040image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:11:59.171291image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:11:59.269073image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:11:59.365225image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:11:59.471870image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:11:59.584152image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:11:59.689141image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:11:59.782913image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:11:59.875855image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:11:59.968796image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:00.064457image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:00.177080image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:00.279496image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:00.377103image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:00.473297image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:00.574984image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:00.687116image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:00.805815image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:00.909442image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:01.023944image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:01.124069image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:01.235397image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:01.347977image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:01.462737image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:01.561916image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:01.694832image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:01.834461image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:01.960909image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:02.074090image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:02.191085image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:02.303647image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:02.410121image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:02.527013image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:02.621702image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:02.983527image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:03.154411image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:03.308557image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:03.443105image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:03.542932image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:03.658850image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:03.782847image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:03.904827image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:04.007009image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:04.106327image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:04.210061image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:04.314210image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:04.416573image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:04.543544image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:04.652142image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:04.750416image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:04.840299image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:04.932560image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:05.025749image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:05.120845image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:05.216394image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:05.313125image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:05.407413image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:05.502584image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:05.621864image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:05.753300image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:05.884114image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:06.005150image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:06.113844image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:06.227101image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:06.336584image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:06.446869image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:06.565053image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:06.675281image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:06.774161image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:06.873704image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:07.055027image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:07.213174image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:07.333653image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:07.445582image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:07.561252image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:07.670602image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:07.782941image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:07.897209image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:08.004566image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:08.124505image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:08.235929image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:08.332869image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:08.449971image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:08.592943image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:08.709478image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:08.822547image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:08.929210image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:09.036374image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:09.156145image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:09.268518image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:09.369234image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:09.471425image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:09.582431image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:09.702827image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:09.809513image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:09.962364image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:10.089735image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:10.209171image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:10.319448image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:10.707689image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:10.849590image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:10.964107image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:11.084096image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:11.207909image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:11.322547image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:11.443728image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:11.567616image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:11.685613image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:11.815994image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:11.945004image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:12.062539image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:12.165561image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:12.266461image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:12.367373image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:12.480185image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:12.629273image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:12.834563image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:12.961752image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:13.080236image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:13.193673image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:13.319166image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:13.427210image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:13.536349image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:13.638664image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:13.747086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:13.867500image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:13.980146image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:14.083384image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:14.175697image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:14.269844image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:14.378593image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:14.486443image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-11T13:12:14.598014image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-04-11T13:12:21.301395image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-04-11T13:12:21.470693image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-04-11T13:12:21.639543image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-04-11T13:12:21.819505image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-04-11T13:12:22.038709image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-04-11T13:12:14.827707image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-04-11T13:12:15.207676image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-04-11T13:12:15.511142image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-04-11T13:12:15.761917image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

SuburbAddressRoomsTypePriceMethodSellerGDateDistancePostcodeBedroom2BathroomCarLandsizeBuildingAreaYearBuiltCouncilAreaLattitudeLongtitudeRegionnamePropertycount
0Abbotsford68 Studley St2hNaNSSJellis03-09-20162.53067.02.01.01.0126.0NaNNaNYarra-37.8014144.9958Northern Metropolitan4019.0
1Abbotsford85 Turner St2h1480000.0SBiggin03-12-20162.53067.02.01.01.0202.0NaNNaNYarra-37.7996144.9984Northern Metropolitan4019.0
2Abbotsford25 Bloomburg St2h1035000.0SBiggin04-02-20162.53067.02.01.00.0156.079.01900.0Yarra-37.8079144.9934Northern Metropolitan4019.0
3Abbotsford18/659 Victoria St3uNaNVBRounds04-02-20162.53067.03.02.01.00.0NaNNaNYarra-37.8114145.0116Northern Metropolitan4019.0
4Abbotsford5 Charles St3h1465000.0SPBiggin04-03-20172.53067.03.02.00.0134.0150.01900.0Yarra-37.8093144.9944Northern Metropolitan4019.0
5Abbotsford40 Federation La3h850000.0PIBiggin04-03-20172.53067.03.02.01.094.0NaNNaNYarra-37.7969144.9969Northern Metropolitan4019.0
6Abbotsford55a Park St4h1600000.0VBNelson04-06-20162.53067.03.01.02.0120.0142.02014.0Yarra-37.8072144.9941Northern Metropolitan4019.0
7Abbotsford16 Maugie St4hNaNSNNelson06-08-20162.53067.03.02.02.0400.0220.02006.0Yarra-37.7965144.9965Northern Metropolitan4019.0
8Abbotsford53 Turner St2hNaNSBiggin06-08-20162.53067.04.01.02.0201.0NaN1900.0Yarra-37.7995144.9974Northern Metropolitan4019.0
9Abbotsford99 Turner St2hNaNSCollins06-08-20162.53067.03.02.01.0202.0NaN1900.0Yarra-37.7996144.9989Northern Metropolitan4019.0

Last rows

SuburbAddressRoomsTypePriceMethodSellerGDateDistancePostcodeBedroom2BathroomCarLandsizeBuildingAreaYearBuiltCouncilAreaLattitudeLongtitudeRegionnamePropertycount
23537Wheelers Hill12 Strada Cr4h1245000.0SBarry26-08-201716.73150.04.02.02.0652.0NaN1981.0NaN-37.90562145.16761South-Eastern Metropolitan7392.0
23538Williamstown77 Merrett Dr3h1031000.0SPWilliams26-08-20176.83016.03.02.02.0333.0133.01995.0NaN-37.85927144.87904Western Metropolitan6380.0
23539Williamstown83 Power St3h1170000.0SRaine26-08-20176.83016.03.02.04.0436.0NaN1997.0NaN-37.85274144.88738Western Metropolitan6380.0
23540Williamstown8/2 Thompson St2t622500.0SPGreg26-08-20176.83016.02.02.01.0NaN89.02010.0NaN-37.86393144.90484Western Metropolitan6380.0
23541Williamstown96 Verdon St4h2500000.0PISweeney26-08-20176.83016.04.01.05.0866.0157.01920.0NaN-37.85908144.89299Western Metropolitan6380.0
23542Wyndham Vale25 Clitheroe Dr3uNaNPNHarcourts26-08-201727.23024.03.01.00.0552.0119.01990.0NaN-37.90032144.61839Western Metropolitan5262.0
23543Wyndham Vale19 Dalrymple Bvd4hNaNShockingstuart26-08-201727.23024.0NaNNaNNaNNaNNaNNaNNaN-37.87882144.60184Western Metropolitan5262.0
23544Yallambie17 Amaroo Wy4h1100000.0SBuckingham26-08-201712.73085.04.03.02.0NaNNaNNaNNaN-37.72006145.10547Northern Metropolitan1369.0
23545Yarraville6 Agnes St4h1285000.0SPVillage26-08-20176.33013.04.01.01.0362.0112.01920.0NaN-37.81188144.88449Western Metropolitan6543.0
23546Yarraville33 Freeman St4h1050000.0VBVillage26-08-20176.33013.04.02.02.0NaN139.01950.0NaN-37.81829144.87404Western Metropolitan6543.0